V. Yogarajan, G. Dobbie, and H. Gouk, “Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis,” ICLR Tinypapers, 2023. doi: 10.48550/arXiv.2304.11094.
An indigenous perspective on the effectiveness of debiasing techniques for pre-trained language models (PLMs) is presented in this paper. The current techniques used to measure and debias PLMs are skewed towards the US racial biases and rely on pre-defined bias attributes (e.g. “black” vs “white”). Some require large datasets and further pre-training. Such techniques are not designed to capture the underrepresented indigenous populations in other countries, such as Māori in New Zealand. Local knowledge and understanding must be incorporated to ensure unbiased algorithms, especially when addressing a resource-restricted society.
Vithya Yogarajan, Research Fellow, School of Computer Science, University of Auckland
Read the full paper here: doi.org/10.48550/arXiv.2304.11094